Decoding network-mediated retinal response to electrical stimulation: implications for fidelity of prosthetic vision

Abstract

Abstract Objective. Patients with photovoltaic subretinal implant PRIMA demonstrated letter acuity ∼0.1 logMAR worse than sampling limit for 100 μm pixels (1.3 logMAR) and performed slower than healthy subjects tested with equivalently pixelated images. To explore the underlying differences between natural and prosthetic vision, we compare the fidelity of retinal response to visual and subretinal electrical stimulation through single-cell modeling and ensemble decoding. Approach. Responses of retinal ganglion cells (RGCs) to optical or electrical white noise stimulation in healthy and degenerate rat retinas were recorded via multi-electrode array. Each RGC was fit with linear–nonlinear and convolutional neural network models. To characterize RGC noise, we compared statistics of spike-triggered averages (STAs) in RGCs responding to electrical or visual stimulation of healthy and degenerate retinas. At the population level, we constructed a linear decoder to determine the accuracy of the ensemble of RGCs on N-way discrimination tasks. Main results. Although computational models can match natural visual responses well (correlation ∼0.6), they fit significantly worse to spike timings elicited by electrical stimulation of the healthy retina (correlation ∼0.15). In the degenerate retina, response to electrical stimulation is equally bad. The signal-to-noise ratio of electrical STAs in degenerate retinas matched that of the natural responses when 78 ± 6.5% of the spikes were replaced with random timing. However, the noise in RGC responses contributed minimally to errors in ensemble decoding. The determining factor in accuracy of decoding was the number of responding cells. To compensate for fewer responding cells under electrical stimulation than in natural vision, more presentations of the same stimulus are required to deliver sufficient information for image decoding. Significance. Slower-than-natural pattern identification by patients with the PRIMA implant may be explained by the lower number of electrically activated cells than in natural vision, which is compensated by a larger number of the stimulus presentations.